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Upper limit for sea level projections by 2100
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2014 Environ. Res. Lett. 9 104008
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Environmental Research Letters
Environ. Res. Lett. 9 (2014) 104008 (9pp)
doi:10.1088/1748-9326/9/10/104008
Upper limit for sea level projections by 2100
S Jevrejeva1, A Grinsted2 and J C Moore3,4
1
National Oceanography Centre, Liverpool, UK
Centre for Ice and Climate, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
3
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and
Earth System Science, Beijing Normal University, Beijing, People’s Republic of China
4
Arctic Centre, University of Lapland, Rovaniemi, Finland
2
E-mail: [email protected]
Received 4 April 2014, revised 19 September 2014
Accepted for publication 19 September 2014
Published 10 October 2014
Abstract
We construct the probability density function of global sea level at 2100, estimating that sea
level rises larger than 180 cm are less than 5% probable. An upper limit for global sea level rise
of 190 cm is assembled by summing the highest estimates of individual sea level rise
components simulated by process based models with the RCP8.5 scenario. The agreement
between the methods may suggest more confidence than is warranted since large uncertainties
remain due to the lack of scenario-dependent projections from ice sheet dynamical models,
particularly for mass loss from marine-based fast flowing outlet glaciers in Antarctica. This leads
to an intrinsically hard to quantify fat tail in the probability distribution for global mean sea level
rise. Thus our low probability upper limit of sea level projections cannot be considered
definitive. Nevertheless, our upper limit of 180 cm for sea level rise by 2100 is based on both
expert opinion and process studies and hence indicates that other lines of evidence are needed to
justify a larger sea level rise this century.
Keywords: sea level rise, high end projections, climate change
1. Introduction
depend on projections of mean sea level and crucially its low
probability, high impact, upper range (Nicholls et al 2014).
Subsequently, many nations, cities and regions have
developed coastal protection plans and guidance on sea level
rise scenarios, including a low probability high impact or
worse case scenario, for use in contingency planning and for
consideration of the limits of potential adaptations. The Delta
Commission in the Netherlands has used projected sea level
of 1.1 m as a ‘high-end’ scenario (Vellinga et al 2008). The
Scientific Committee on Antarctic Research (SCAR) suggested up to 1.4 m sea level projections by 2100 (Scientific
Committee on Antarctic Research 2009) and the Arctic
Monitoring and Assessment Programme (AMAP) considered
1.6 m in its latest report (Arctic Monitoring and Assessment
Program (AMAP) 2012). The US Army Corps of Engineers
used a 1.5 m rise by 2100 as a ‘high’ scenario for planning
civil works programmes (US Army Corps of Engineers 2011), with sea level rise up to 2 m used in US National
Climate Assessment (NOAA Technical Report OAR CPO1 2012) and 1.9 m considered in the United Kingdom Climate
Impacts Programme (UKCIP) (Lowe et al 2009).
With more than 600 million people living in the low elevation
coastal areas less than 10 m above sea level (McGranahan
et al 2007), and around 150 million people living within 1 m
of high tide (Lichter et al 2011) future sea level rise is one of
the most damaging aspects of warming climate (Anthoff
et al 2009, Hallegatte et al 2013). The latest Intergovernmental Panel on Climate Change report (AR5 IPCC) noted
that a 0.5 m rise in mean sea level will result in a dramatic
increase the frequency of high water extremes—by an order
of magnitude, or more in some regions (Church et al 2013a).
Thus the flood threat to the rapidly growing urban populations
and associated infrastructure in coastal areas are major concerns for society (Hallegatte et al 2013, Hinkel et al 2014).
Hence, impact assessment, risk management, adaptation
strategy and long-term decision making in coastal areas
Content from this work may be used under the terms of the
Creative Commons Attribution 3.0 licence. Any further
distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
1748-9326/14/104008+09$33.00
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© 2014 IOP Publishing Ltd
Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
emission scenarios used in each report. Several centimetres of
variation can be explained by the different reference periods
chosen. Larger differences come from the components of the
sea level budget included in each report. For example, the
0.59 m rise projected under the Special Report on Emissions
Scenarios (SRES) A1FI, scenario in AR4 does not include the
contribution of ice sheet dynamics in Greenland and Antarctica, which the report explicitly stated could contribute up
to a further 0.20 m (Meehl et al 2007).
In all five IPCC reports sea level projections have been
assembled using the conventional method of estimating sea
level rise—by simulating contributions from individual sea
level components, such as thermal expansion, and melting ice
from glaciers and ice sheets. The latest AR5 IPCC report
(Church et al 2013a) provides sea level projections spanning
a likely range (66%) and with medium confidence only,
implying there is a probability of about 1/3 that sea level rise
may lie outside the stated uncertainty ranges (Church
et al 2013b) largely due to difficulties in projecting ice mass
loss from the Greenland and Antarctica ice sheets. The AR5
addresses this issue by suggesting that ‘only the collapse of
the marine-based sectors of the Antarctic ice sheet, if initiated,
could cause sea level to rise substantially above the likely
range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium
confidence that it would not exceed several tenths of a metre
of sea level rise’ (Church et al 2013a). Therefore the upper
bound of 0.98 m given in AR5 for the highest emission
Representative Concentration Pathways scenario (RCP8.5)
should not be misconstrued as a worst-case projection
(Church et al 2013b). In this article we attempt to quantify the
worst-case projection of sea level rise.
In contrast to the IPCC likely range (66%) estimates of
sea level rise, the upper limit or worst-case sea level rise
projections, by definition, are low probability estimates,
unlikely to be reached, but which at the same time, cannot be
ruled out given paleoclimate proxy observations and processbased modelling limitations. The tail of the probability distribution function for future sea level calculated from each of
the individually modelled components has not been wellexplored meaning that reliable estimates from process-based
modelling of the upper limit by 2100 are difficult to provide.
Evidence from expert elicitations (Bamber and Aspinall 2013,
Horton et al 2014) suggests that the uncertainty in model
projections is highly skewed towards greater potential sea
level rise.
In this paper, we construct a probability density function for global mean sea level rise by the year 2100 and
estimate the sea level rise having a less than 5% probability,
which we suggest is taken as the upper limit for sea level
rise. In addition, we assemble the upper limit for possible
sea level rise by 2100 from process based model outputs for
individual sea level components. This allows us to assess
whether the highest estimates of 1.5–2.0 m published in
various national reports can be supported by process-based
modelling.
Figure 1. The range of global mean sea level projections with
different high emission scenarios by 2100 from five IPCC reports
(bars): a scenario from FAR; IS92e scenario from SAR; A1FI SRES
from TAR and AR4 (contribution from ice sheet dynamics is not
included in AR4 projection), RCP8.5 from the AR5. Black
horizontal lines represent the low probability high impact range
scenarios used in the national planning from alternative methods to
estimate possible sea level rise (1.1 m is the Delta Commission,
1.4 m is SCAR; 1.9 m is H++ scenario in UKCIP; 2 m is NOAA).
These high-end estimates used as worst-case scenarios
in national planning come from several alternative methods
of estimating sea level rise. Scenario H ++ (1.9 m) in
UKCP09 is based on paleo information on sea level rise
(Lowe et al 2009). A wide range of simulations from semiempirical models (SEMs) (Rahmstorf 2007, Grinsted
et al 2010, Jevrejeva et al 2012a), and consideration of
kinematic limits on ice throughput from land to ocean
(Pfeffer et al 2008, Katsman et al 2011) have also been
used as upper bounds. The upper limit scenarios are an
essential tool for scientists, engineers and policy analysts
tasked with designing responses and adaptation strategy to
sea level rise.
Since 1990 each of the IPCC reports have produced a
wide range of projections for sea level at the year 2100 with
high-end greenhouse gas emission scenarios (figure 1) producing sea level rises ranging from 0.59 m in the Fourth
Assessment Report (AR4) (Meehl et al 2007) to 1.24 m in the
First Assessment Report (FAR) (Houghton et al 1990,
Houghton et al 1996). Some of the differences in these high
end sea level projections may be attributed to the changing
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Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
2. Results
2.1. Sea level rise beyond the likely range
The conventional approach to project sea level rise is based
on simulation of individual sea level components: contributions from ocean thermal expansion and melting/ dynamics of
glaciers and the ice sheets, and then sum them up (Meehl
et al 2007, Solomon et al 2009, Church et al 2013a). In our
study, we follow this approach by considering projections of
the main sea level components:
•
•
•
•
•
Thermal expansion.
Glacier surface mass balance (SMB).
Greenland SMB and dynamical changes.
Antarctica SMB and dynamical changes.
Changes in land water storage.
The main challenge in providing the upper limit (taken as
having only 5% likelihood of being exceeded) for sea level
rise by 2100 is to quantify the uncertainty beyond the likely
range of sea level projections. The lack of robust simulations
of the ice sheet contribution to global sea level rise from
process based models led to only a likely range (66%) and
with medium confidence being given for sea level projections
in the recently released AR5 IPCC report (Church
et al 2013a). This range leaves roughly a 30% chance of sea
level rise being outside the stated uncertainty ranges (Church
et al 2013b). Thus, the IPCC projections did not exclude the
possibility of higher sea levels; AR5 concluded that ‘sea
levels substantially higher than the ‘likely’ range would only
occur in the 21st century if the sections of the Antarctic ice
sheet that have bases below sea level were to collapse’
(Church et al 2013b). Post-AR5 modelling indicates that Pine
Island Glacier in West Antarctica is probably already engaged
in an unstable retreat (Favier et al 2014), a situation that is
projected to extend to the neighbouring Thwaites glacier
(Joughin et al 2014) and also to the Totten and other glaciers
in East Antarctica (Sun et al 2014). Over the 21st century the
sea level rise contributions from these glaciers are well within
the ‘likely’ range of the AR5 estimate, with ice loss rates
increase over the century, consistent with observations that
show a widespread and sustained increase in discharge in the
Amundsen Sea sector (Mouginot et al 2014, Rignot
et al 2014).
To explore the uncertainties in sea level projections
beyond the likely range, we blend the approach from AR5
IPCC report (Church et al 2013a) with expert assessment of
Greenland and Antarctica ice sheet contributions. The expert
elicitation approach has been widely used in various social
science and economic impact assessments for many years, for
example in the Dynamic Integrated Climate-Economy (DICE)
climate change damage model by Nordhaus and Boyer
(1999), but has not been widely used in the physical sciences.
We regard expert judgement as a useful tool to assess the
uncertainty ranges, because the ice sheet experts know which
physical processes (e.g. calving, ice sheet-ocean interaction)
are insufficiently represented in their ice sheet models. Hence,
the expert solicitation is able to explore regions of known
Figure 2. Projected component of global sea level rise by 2100
relative to 2000 and their uncertainty. Vertical light grey bars
indicate the 5, 50 and 95th percentiles in the uncertainty distribution.
Dark grey bars represent projected sea level components calculated
in this study. Thick red lines show the likely range of the sea level
contributions from the AR5 and red thin lines are our fit to the AR5
distribution assuming symmetric tails.
ignorance which are traditionally neglected in published
process model results (Oppenheimer et al 2008). In the case
of ice sheet dynamics, (Little et al 2013) suggest ‘In the
absence of robust data or appropriate models, collapse probabilities may be assessed using formalized expert elicitation
(Bamber and Aspinall 2013)’. The Bamber and Aspinall
(2013) study has been used to derive the uncertainty range of
the rate of mass loss from each ice sheet at 2100, and to
determine the degree of consensus about these uncertainties
within the ice sheet expert community. We do not claim that
this is complete or perfect, but this is a source for the
uncertainty estimate, and it gives an opportunity to explore
the uncertainties in the contribution from ice sheets, which are
simply not available from other sources. The advantage of
using Bamber and Aspinall (2013) is that we may escape the
problem of ‘single study syndrome’ or having to assess the
relative merits of different studies. Bamber and Aspinall
(2013) quantify the expert community uncertainty at that
time. We acknowledge that this may have changed since its
publication. For example, it is quite possible that the recent
series of studies of the Amundsen Sea Sector and West
Antarctic ice sheet collapse will alter expert opinion.
From the AR5 uncertainty distribution of each individual
sea level component, that is thermal expansion, melting of
glaciers, water land storage and ice sheets we draw samples
using a Monte Carlo method. We reproduce the AR5 projection uncertainty ranges using uniformly distributed
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Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
ice loss with, for example a global mean temperature rise of
2 °C or 4 °C. By using the probability distribution functions
for ice sheets from Bamber and Aspinall (2013), we
acknowledge and accommodate the collective view of the
thirteen ice sheet experts that mass loss during the 21st century more than doubles with temperature increasing from 2 °C
to 4 °C (Bamber and Aspinall 2013).
We estimate an upper limit of 180 cm for the global sea
level rise with probability of 5%; the median (50%) in our
probability distribution function is 80 cm, which is close to
the median of 73 cm in AR5. An important caveat of our
approach utilizing results based on expert opinion is that
predictions by experts are often overconfident (Capen 1976).
Therefore we ask the question: could we support our upper
limit estimate of 180 cm by the results from process based
model simulations?
Figure 3. Projected global mean sea level rise by 2100 relative to
2000 for the RCP8.5 scenario and uncertainty. Vertical grey bars
indicate the 5, 17, 50, 83, and 95th percentiles in the uncertainty
distribution.
2.2. High end estimates of sea level rise from process based
model simulations
uncertainties for the contributions from land water storage and
ice sheets, and a multivariate normal distribution for the
thermal expansion and glaciers (figure 2, red lines). The
uncertainty covariance structure is fitted to be consistent with
the AR5 likely ranges for each individual contributor and
their sum (Church et al 2013a). We emphasize that AR5 did
not specify the shape of the uncertainty distribution and
explicitly stated that sea level rise beyond the likely range is
poorly constrained and considerably larger increases are
possible.
From AR5 we adopt the uncertainty distributions for the
thermal expansion, glaciers and water land storage. However,
we replaced the AR5 projection uncertainties for both ice
sheets with probability distribution function calculated from
the collective view of thirteen ice sheet experts (Bamber and
Aspinall 2013). The ice sheet expert elicitation allows construction of the covariance and shape of the uncertainty distribution of the rate of each ice sheet mass loss in 2100
(Bamber and Aspinall 2013), which we Monte Carlo sample
to obtain the rate of mass loss by Antarctica and Greenland in
2100. We calculate the net sea level rise by integrating ice
sheet mass loss rate assuming a linear increase in these rates
from their present day values taken from the comprehensive
review of Shepherd et al (2012). Figure 2 panels for Greenland and Antarctica, show the difference in probability distributions from the approach in this study (grey colour), the
AR5 likely range (red thick line), and our symmetric fit to the
AR5 projections (red thin line). In particular, there is a large
difference in the shape of the uncertainty distribution for the
Antarctic ice sheet, with a long skewed fat-tail at the high rise
end of the distribution.
We combine the uncertainty distributions for each component (figure 2) and construct the probability distribution
function for global sea level rise by 2100 (figure 3). This
combined distribution has a fat-tail. Roe and Baker (2007)
suggest that such a distribution is a ubiquitous, and inevitable
feature of the climate system (or at least how we observe it).
The AR5 ice sheet contribution to sea level rise is almost
climate-scenario independent, suggesting the same amount of
In table 1 we present the highest estimates of projected sea
level components from the latest publications with projections
driven by the RCP8.5 scenario, which is the highest radiative
forcing used by AR5. Current climate models are in reasonably good agreement on predicting ocean thermal expansion
(Yin 2012, Church et al 2013a), with a thermosteric sea level
for RCP8.5 scenario calculated from 34 models of 32 cm,
with 5–95% range of 25–39 cm (Church et al 2013a; AR5
supplementary material, datafiles for chapter 13, www.
climatechange2013.org/report/full-report/).
A recent development in glacier modelling has been the
compilation of the Randolph Glacier Inventory (RGI), which
covers the entire world for the first time (Arendt et al 2012).
Here we consider only projections of the contribution from
glaciers to sea level rise published since the RGI has been
available. The model by Marzeion et al (2012) is the most
temperature sensitive of the glacier projections and results in
more ice loss than simulations from Radic et al (2013) or
Giesen and Oerlemans (2013). However, the projections in
Marzeion et al (2012) do not include ice loss from relatively
small glaciers fringing the Antarctic periphery, which could
increase contribution by variously 14%, 23%, 18%, or 22% as
estimated by Giesen and Oerlemans (2013), Radic et al
(2013), Slangen and van de Wal (2011), and Radic and Hock
(2011) respectively. For our upper limit estimate we use a
contribution of 35 cm calculated from the largest projection of
28 cm from Marzeion et al (2012) adjusted for a 20% missing
Antarctic fraction (see table 1). However, the glacier models
do not adequately account for several processes, particularly
calving (Cogley 2009, Jevrejeva et al (2012b), Moore
et al 2013). As a check on the process models we can also
estimate dynamical changes with a different rough and ready
approach: the total area of glacier basins (excluding the
Greenland and Antarctic ice sheets) draining through marine
outlets is about 280 000 km2 (Gardner et al 2013). If we
assume a basin thinning rate in all these marine outlets of
5 m yr−1, the same as rate of the ice loss for Columbia Glacier
since 1982 (Rasmussen et al 2011), then at most 30 cm would
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Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
Table 1. The highest estimates of projected sea level components by 2100 relative to 2000 with RCP8.5 scenario (or other high end scenario,
or scenario independent estimate described under ‘comments’).
Contributors
Thermal expansion (T), (Yin 2012)
Glaciers, SMB (GSMB), (Marzeion
et al 2012)
Greenland SMB (GrSMB) (Fettweis
et al 2013)
Greenland dynamics (GrD), (Bindschandler et al 2013)
Antarctica SMB (ASMB), (Church
et al 2013a)
Antarctica dynamics (AD), (Hinkel
et al 2014)
Land water (LW), (Church et al 2013a)
AR5 RCP8.5 likely
range (cm)
Highest estimate (cm)
25–39
10–26
39
35
4–22
20
2–9
44*
−9– −2
−2
−2–19
41
High land ice scenario, four models
−1–11
11
Scenario independent
Comments
CMIP5, 34 AOGCMs
SMB only, no ice dynamics, (adjusted for a 20%
missing Antarctic fraction, see text)
High end scenario, SMB is excluded (see text)
Note: * Estimate of 44 cm (66–22 = 44 cm) calculated as a difference between the total contribution from Greenland ice sheet (SMB+ice dynamics,
Bindschandler et al (2012)) of 66 cm and 22 cm of SMB taking from AR5 (Church et al 2013a, AR5 supplementary material, datafiles for chapter 13, www.
climatechange2013.org/report/full-report/).
RCP8.5, which included imposed changes in ice-shelf basal
melting and ice sheet basal sliding. The average contribution
from Greenland was 22 cm and the largest was 66 cm, however, these estimates included the SMB contribution as well,
which may be up to 22 cm (Church et al 2013a, AR5 supplementary material, datafiles for chapter 13, www.
climatechange2013.org/report/full-report/). Therefore we use
44 cm as the ice dynamics contribution from Greenland in
table 1. For Antarctica the largest dynamical contribution
from ice sheet is 41 cm, as reported in recently published
estimates from four models forced with RCP8.5 (Hinkel
et al 2014). These ice dynamics estimates are far beyond the
IPCC likely range (see table 1).
The sum from the sea level component projections by
2100 in table 1, obtained as the highest estimates from process based models with high emission scenarios,
(39T + 35GSMB + 22GrSMB + 44GrD − 2ASMB + 41AD + 11LW) is
190 cm. These simple calculations demonstrate that using the
highest estimates from process based models for individual
sea level components (largely published after the AR5 and
Bamber and Aspinall 2013), provides support for the upper
limit of sea level projection by 2100 of 180 cm, estimated
from the probability analysis in section 2.1.
be added to global sea level by 2100. This is a very extreme
possibility, if only 30% of these marine outlets will lose ice
mass due to dynamical changes, then 10 cm may be added.
This estimate is far too crude to use as a quantified estimate, it
simply illustrates the range that glacier calving may contribute
to sea level. We do not add any such contribution to our
estimate of the upper limit from process based model
simulations.
Greenland ice sheet SMB has been modelled with the
regional climate model (Modèle Atmosphérique Régional,
(MAR)) by Fettweis et al (2013), which suggests that SMB is
strongly correlated with global mean surface warming. An
upper limit of 20 cm for Greenland SMB was calculated using
the range of projected global surface temperatures under the
RCP8.5 scenario (Fettweis et al 2013), we adopt the upper
bound of 22 cm from IPCC AR5 (Church et al 2013a, AR5
supplementary material, datafiles for chapter 13, www.
climatechange2013.org/report/full-report/). For Antarctica
simulations suggest that increased snow fall in warming climates dominates rising melt water run-off, therefore SMB
contributes negatively to global sea level rise over the 21st
century (Ligtenberg et al 2013). We adopt the upper bound of
−2 cm from IPCC AR5 (Church et al 2013a, AR5 supplementary material, datafiles for chapter 13, www.
climatechange2013.org/report/full-report/), as there appears
to be no support from the current generation regional climate
modelling for larger contributions (Ligtenberg et al 2013).
Greenland and Antarctica ice sheets also contribute to sea
level via the dynamic discharge of ice into the ocean. Several
studies show that this dynamic discharge can respond to
changes in climate (Hellmer et al 2012, Bindschadler
et al 2013, Hinkel et al 2014). However, the coupling
between the ice sheet dynamics and climate is a challenging
task (Moore et al 2013). This is illustrated by the almost
constant projections for the ice sheet dynamical contribution
in the latest AR5 IPCC (Church et al 2013a). Bindschadler
et al (2013) forced an ensemble of ice sheet models with a
bespoke ‘R8’ scenario intended as an approximation to the
3. Uncertainties in upper limit estimates
3.1. Uncertainties associated with emission scenarios
There are several sources of uncertainties in upper limit of sea
level projections. The first key uncertainty is high emission
scenario itself, in which the implications of anthropogenic and
natural climate change for environment and society depend
not only on the response of the Earth system to radiative
forcing, but also on the potential responses by humans
through changes in economies, technology, policy and lifestyle, which are largely unknown and therefore impose large
uncertainties. In addition, climate models have many other
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Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
uncertainties: climate sensitivity to radiative forcing, complex
feedbacks, large internal climate variability and the parameterization of many processes. Alternative approaches, such
as kinematic limits (Pfeffer et al 2008, Katsman et al 2011)
provide scenario independent assessment of maximum physically plausible contributions from ice sheets, glaciers and
thermal expansion.
This range of response times spans the typical time constants
of the main sea level reservoirs, representing the responses of
small glaciers, thermal expansion of the ocean, and ice-sheet
response to changes in radiative forcing given by the RCP
scenarios which can be used to provide upper and low limits
for sea level projections at the year 2100. The most sensitive
SEMs give upper bound of 160 cm (Church et al 2013a,
Rahmstorf et al 2012, Grinsted et al 2010), providing good
agreement with the probabilistic upper limit of 180 cm
(section 2.1) and 190 cm from process based model outputs
(section 2.2).
3.2. Uncertainties in ice sheet contribution to future sea
level rise
As we have seen, the crucial question for sea level rise in the
twenty-first century is how much ice will be lost from the
Greenland and Antarctic ice sheets as a result of rapid
accelerations in ice dynamics. Huge progress has been made
in understanding of ice dynamics (Moore et al 2013, Church
et al 2013a), ice stream flow (Bougamont et al 2011),
grounding line migration (Schoof 2011, Docquier et al 2012,
Drouet et al 2012) and integration of ice sheet models with
high-resolution climate models (Cornford et al 2013). However, currently there is only limited number of model simulations of ice sheet contributions with RCP-like scenarios
(Bindschadler et al 2013, Nick et al 2013). The role of icesheet ocean interaction is one the main challenges for the ice
sheet modelling community. Lack of scenario dependent ice
sheet model runs impose difficulties in exploring the ranges of
uncertainties in potential contributions from ice sheets and the
alternative approach suggested by Bamber and Aspinall
(2013) illustrates the magnitude of uncertainties in ice sheet
mass loss projections by 2100. In fact, ice sheet experts
suggest possible contributions from both ice sheets exceeding
84 cm at 5% probability by 2100 (Bamber and Aspinall 2013). This number is consistent with our estimate of
highest contribution from ice sheets (table 1).
4. Conclusion
We constructed the probability density function for global sea
level rise at 2100 and calculate that a rise of 180 cm has only
a 5% probability of being exceeded. This estimate is supported by the 190 cm of sea level rise calculated using the
high end estimates from process based models for individual
sea level components forced by the RCP8.5 scenario (table 1).
Large contributions from the Antarctic (41 cm) and Greenland
(46 cm) ice sheets simulated by process based models
(Bindschadler et al 2013, Hinkel et al 2014) and published
after the expert elicitation study of Bamber and Aspinall
(2013) are consistent with the expert opinion of an 84 cm
contribution from ice sheets. The largest uncertainties in sea
level projections are due to the limited number of ice sheet
models able to drive changes in ice sheet dynamics with
climate forcing (Moore et al 2013, Church et al 2013a,
Bindschadler et al 2013, Nick et al 2013, Hinkel et al 2014).
Nevertheless, there is evidence that ice shelves in West
Antarctica are not stable but are thinning rapidly (Pritchard
et al 2012), and that the large outlet glaciers that drain the
West Antarctic ice sheet have been accelerating in recent
decades, most likely as a result of increased melting of their
ice-shelf termini by warm Circumpolar Deep Water (Steig
et al 2012).
In this paper we suggest that there is a 95% chance that
mean sea levels will not exceed 180 cm above those at present. This will vary regionally due to various factors such as:
(1) groundwater depletion causing local subsidence (an acute
problem in some cities such as Bangkok); (2) glacial isostatic
rebound leading to some cities rising above mean sea level
(such as Stockholm); (3) regional sea level disparity due to
preferential melt of Greenland or Antarctic ice sheets and the
self-gravitational effect (Church et al 2013a). Assessing how
to deal with the impact and costs of sea level rise poses
serious structural issues in economic cost-benefit analysis
(Weitzman 2009), but widely used assumption is of a quadratic function for climate change impacts (Nordhaus 2008,
Weitzman 2010). The annual damage costs for the European
Union with sea level rise of 1.4 m by 2100 are projected to be
six times greater than for the rise of 0.6 m with A1B climate
scenario (Brown et al 2011). However, Weitzman (2009), in
his ‘Dismal theorem’ shows that if the rate of decay of the
probability tail of the climate impact function is polynomial
while the cost of damage rises exponentially, then the cost-
3.3. Use of SEMs to explore the range of uncertainties
SEMs have been developed to make projections of sea level
rise since 2007. Instead of projecting individual components
of sea level separately SEMs consider changes in global sea
level as an integrated response of the entire climate system to
changes in temperature (Rahmstorf 2007, Vermeer and
Rahmstorf 2009, Grinsted et al 2010) or radiative forcing
(Jevrejeva et al 2010). Projections by SEMs are based on the
assumption that sea level in the next 100 years will respond as
it has in the past 100–300 years (Vermeer and Rahmstorf 2009, Grinsted et al 2010, Jevrejeva et al 2010) or even
1000 years (Rahmstorf et al 2012), to imposed climate forcing. This may not hold in the future if potentially nonlinear
physical processes, such as marine ice-sheet instability or
thermal expansion do not scale in the future as they have in
the past. However, SEMs by design generate millions of
potential sea level projections with model parameters selected
randomly from amongst their distributions, exploring a wide
range of sea level projections and generating 5–95% confidence intervals from the millions possible realizations. For
example, the response time parameter varies from 10–5000
years in sea level simulations by 2100 (Jevrejeva et al 2012a).
6
Environ. Res. Lett. 9 (2014) 104008
S Jevrejeva et al
improved our manuscript. This publication has received
funding from the European Union’s Seventh Programme for
Research, Technological Development and Demonstration
under Grant Agreement No: FP7-ENV-2013-Two-Stage603396-RISES-AM, NERC consortium ‘Using Inter-glacial
to assess future sea level scenarios’ (NE/I008365/1), from
Beijing Normal University, College of Global Change and
Earth System Science, State Key Laboratory on Earth Surface
Processes and Resource Ecology, 270403 GK and the Danish
Strategic Research Council through its support of Centre for
Regional Change in the Earth System (CRES; www.crescentre.dk) under contract no DSF-EnMi 09-066868.
benefit function does not converge and no cost of mitigation
is too high to justify. As financial infrastructure is being
concentrated at faster rates in urban areas than in countries as
a whole (Anthoff et al 2009), and since the majority of large
cities are located along coastlines, the implications for planning of the Dismal theorem should be considered in addition
to the 5% high end estimate we provide. Sea level rise also
damages agricultural land and taints sweet water supplies,
both of which are essential to maintain quality of city life, and
the net economic impact from high end sea level rise results
from all these damage functions. Large scale human migration is tending to create dense coastal urban settlements,
especially in the developing world. Potential over-concentration of people and resources in coastal zones at risk
from flooding may reverse this trend, or at least cause planners to consider relocating infrastructure to higher, inland,
regions. In countries where this is not possible, increased
cross-border migration pressure will be inevitable.
The upper limit of 180 cm reflects our view of the present
understanding of the uncertainties in Greenland and Antarctica ice loss. This is a very active field, and the focus on ice
sheet dynamics is increasing, although the size and number of
modelling groups are still tiny compared with Earth System
Model centres. Ice dynamics uncertainty is likely to be
reduced if a broader and more diverse group prioritorizes the
research. For example, a key issue is that the calving of icebergs into the sea and the collapse of ice shelves are intrinsically discrete events with timescales on the order of seconds
and fractures propagate near the speed of sound, while typical
ice flow is of the order of metres per year. These problems
require novel approaches such as statistical parameterizations
based on ideas from quantum physics (Bassis 2011) or
numerical models adapted from studies of brittle media
(Åström et al 2013). While these, and other approaches
mature, better flood risk and adaptation planning may come
from using expert elicitation to map the full uncertainty
distribution.
It is clear from the wide range of nationally and locallyadopted maximum rises that engineers and planners find it
difficult to choose appropriate extremes for sea level rise, and
we hope that our approach in this paper can help towards a
methodology in this regard. Although uncertainties are large
now, they will be revised (higher or lower) in future as
models develop. It is however certain that sea level rise will
continue beyond 2100, and this timescale is of importance in
coastal adaptation and mitigation planning. The speed of rise
and the absolute rise at any given date are related views of the
same phenomenon. A sea level rise with only a 5% probability at the year 2100 is far more likely to have occurred by
the year 2200 because of the centennial-scale response times
of the ice-ocean system.
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